In digital libraries queries are often based on the similarity of objects, using several feature attributes like colors,
texture or full-text searches. Such multi-feature queries return a ranked result set instead of exact matches.
Recently we presented a new algorithm called Quick-Combine for combining multi-feature result lists, guaranteeing the correct retrieval of the k top-ranked results. As benchmarks on practical data promise that we can dramatically improve performance, we want to discuss interesting applications of Quick-Combine in different areas. The applications for the optimization in ranked query models are manifold. Generally speaking we believe that all kinds of federated searches can be supported like e.g. content-based retrieval, knowledge management systems or multi-classifier combination.